AI Practitioner

When developing a machine learning or deep learning model, it's critical to know how well the model performs. This requires more than intuition—it needs measurable evaluation metrics. Accuracy alone is often insufficient, especially in imbalanced datasets where the majority class dominates. Consider a cancer diagnosis model. If 99% of patients are healthy, a model predicting everyone as healthy achieves 99% accuracy. However, it fails to detect actual patients, rendering it ineffective. This is where metrics like Precision, Recall, and F1 Score become invaluable. 1. Understanding the Confusion Matrix Evaluation metrics are derived from the confusion matrix, which summarizes prediction outcomes: Actual Positive Actual Negative Predicted Positive TP (True Positive) FP (False Positive) Predicted Negative FN (False Negative) TN (True Negative) These four values form the foundation of most classification metrics. ...